utilization

Compare ownership, colocation and rental using the same useful work, full annual costs and realistic demand. Work a break-even from explicit assumptions.

Compute MFU from a declared global token count and time interval. Check the accounting before judging the percentage or choosing an optimization.

Thirty percent GPU utilization means kernels ran during about thirty percent of the sampled time. Find out what limits those kernels, what happens between them, and whether requests are meeting their latency targets.

The output forecast is a starting point. Match the prompt and state workload, derive replica counts from an SLO-tested rate, check simultaneous loss scenarios and keep the cost denominator consistent with the forecast.

Price the same workload and service guarantees, including input tokens, idle capacity, failures and operations. Then check whether the algebraic crossover fits the fleet before treating it as a buying decision.

Compute cost from a matched bill and token count. Show when an activity factor belongs in the denominator and when it would double-count idle time.

Compare ownership with the actual rental contract, using explicit cash flows, resale assumptions and the hours that can really be avoided.

Size for a tested peak and a stated failure, then price an explicit daily scaling schedule. Keep request-slot occupancy separate from GPU utilization.

Compare the cost of committed capacity with the demand each additional GPU would actually serve. Keep billing discounts separate from capacity guarantees, and account for bursts without counting the same work twice.

Count feasible placements for the waiting job, including its per-node and topology requirements. Then compare waiting, reservations and checkpoint/restart consolidation using their actual disruption costs.

First align the time window and denominator. Then attribute allocated capacity, kernel activity and useful progress to jobs before choosing between reclaim, queue policy and performance work.

Use DCGM fields with their actual semantics, combine device health with workload progress, and calibrate alerts on correlated fleet data.

Show a cost reduction with comparable service evidence, realized savings and a feasible recovery plan. Distinguish a quiet trial from proof of unchanged reliability.

Evaluate a proposed 2,000-GPU purchase using compatible capacity, dated demand and complete alternatives. Make any gap left by reclaim visible.

Defend a specific infrastructure opinion with scoped evidence, useful arithmetic and a condition that would change your view.